PATH-14. SIZE OF INCIDENTALLY DISCOVERED DIFFUSELY INFILTRATING GLIOMAS PREDICTS FUTURE GROWTH RATE
Bibliographic record
Abstract
Incidentally discovered diffusely infiltrating gliomas (iDIGs) are lesions found on brain imaging for a reason not attributable to the lesion. Patients are usually asymptomatic at time of discovery. Incidentally DIGs can harbor different genetic abnormalities and vary widely in size at diagnosis. We hypothesized that tumor size at the time of diagnosis and histologic subtypes predict growth rate for these tumors. We reviewed all low-grade glioma cases at our institution. We identified 455 gliomas treated surgically from 2003 to 2016. Of these, 24(0.05%) cases were found to be iDIGs and to have serial imaging. Serial tumor volumes at minimum 3 month intervals were measured using OsiriX® software. Regions of interest were demarcated on axial FLAIR/T2 MRI sequences. The characteristics of the tumor (molecular genetics, extent of resection) were compiled. We compared tumor size at the time of initial diagnosis of these 24 iDIGs within pathologic subgroups and computed their respective growth rate using serial tumor volume measurement. The median growth rate of our series of iDIGs is 2.41mm/year. The median tumor size at initial diagnosis was 36.8 cm3. We separated iDIGS into small (<36.8 cm3) and large (>36.8 cm3) tumors. There were 5 large astrocytomas and 7 small astrocytomas. The mean growth rate at initial diagnosis for large astrocytomas (n=7) was 3.70 mm/year compared to 2.93 mm/year for small astrocytomas (n=5). By comparing the large and small oligodendrogliomas (n=2; n=9), larger oligodendroglioma grew at a faster rate (2.88 mm/yr vs 2.75 mm/yr). Also, when small astrocytomas were compared to small oligodendrogliomas, growth velocity was faster for the astrocytomas (2.93 mm/yr vs 2.75 mm/yr). Both size at identification and histologic subtype of iDIGS indicates future growth rate. Since growth rate velocity is a predictor for malignant transformation early treatment may impact overall survival.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".